A hybrid automated detection of epileptic seizures in EEG records

doi:10.1016/j.compeleceng.2015.09.001

Computers & Electrical Engineering

Volume 53, July 2016, Pages 177-190

https://doi.org/10.1016/j.compeleceng.2015.09.001 Get rights and content

Highlights

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A new automated seizure detection model is proposed.
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Applying Weighted Permutation Entropy to classify raw and decomposed EEG signals.
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Simulating physiological and environmental artifacts to test the model robustness against noise.
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Comparison between Support Vector Machine and Artificial Neural Network classifier.
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Comparison with other automatics seizure detection models found in literature.

Abstract

The paper introduces a new automated seizure detection model that integrates Weighted Permutation Entropy (WPE) and a Support Vector Machine (SVM) classifier model to enhance the sensitivity and precision of the detection process. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG. The new suggested model better tracks abrupt changes in the signal and assigns less complexity to segments that exhibit regularity or are subjected to noise effects. The Weighted Permutation Entropy algorithm relies on the ordinal pattern of the time series along with the amplitudes of its sample points. The proposed technique is implemented and tested on hundreds real EEG signals and the performance is compared based on sensitivity, specificity and accuracy. Various experiments have been applied in different scenarios including healthy with eyes open, healthy with eyes closed, epileptic patients during no-seizure state from two different location of the brain. Other scenarios have been applied accompanied by background simulated noise resulting from physiological and environmental artifacts. Results showed outstanding performance and revealed promising results in terms of discrimination of seizure and seizure-free segments. It also manifests high robustness against noise sources.

Introduction

Epilepsy is a common long-term neurological condition in which nerve cell activity in the brain becomes disrupted. According to the WHO [1], about 0.6% of the general population suffers from epilepsy and nearly 80% of the affected people are found in developing regions. It manifests clearly through “epileptic seizure”, a period of sudden recurrent and transient disturbances of perception or behavior resulting from excessive synchronous activity of neurons in the brain. Only two third of epileptic patients can control seizures through medications. The remaining third develop drug-resistant epilepsy and are referred for a resection surgery where seizure onset zone (SOZ) is removed. Candidates for the surgery undergo many pre-surgery investigations where the most important is the long term Electroencephalographic (EEG) monitoring to capture seizures for off-line analysis. Expert neurologists then visually inspect the collected EEG data to detect epilepsy-related activity so they can determine the area where the seizures begin, called the seizure focus, and hence conclude whether the surgery is feasible [2]. The EEG has been a very useful clinical tool; it measures the electrical activity and represents potential differences from different sites of the human brain. EEG video monitoring generates a lengthy EEG recordings data for the clinical neurophysiologists to review and analyze seizures that occurred during the monitoring period. However, locating epileptic activity in a continuous EEG recording lasting several days or weeks is an exhausting, demanding and time-consuming task because such activity represents a small percentage of the entire recording. In an earlier study [3], that included 4 human experts to mark an EEG of 8 h, it was shown that here was little variation between readers. High seizure rate, short seizure duration and long seizure durations with ambiguous offsets can complicate the analysis and result in poor correlation. These difficulties have motivated the development of automated methods that scan, identify, and then present to a neurophysiologist epochs containing epileptic events. Such systems help to overcome the limitations of the traditional visual inspection performed by expert neurologists and avoid any misreading or missing information.

This paper introduces an automated seizure detection model that integrates Weighted Permutation Entropy (WPE) as input feature to a Support Vector Machine (SVM) learning model to enhance the sensitivity and precision of the detection process. WPE is a modified statistical parameter of the permutation entropy (PE) suggested by Bandt and Pompe [4]. It measures the complexity and irregularity of a time series by combining the ordinal pattern and the amplitude of its sample points. The feasibility of WPE as a feature for automated seizure detection has not been investigated so far.

The paper is organized as follows: Section 2 provides a cover of some related work presented in literature. Section 3 describes the medical background of epilepsy and brain seizures onto which the work was built, while Section 4 introduces the proposed seizure detection model and the dataset used. The model validation and experiments are demonstrated in Section 5. Results and discussion are illustrated in Sections 6 and 7, respectively. Finally, conclusion and suggestions for future work are illustrated in Section 8.

Section snippets

Literature survey

The automatic seizure detection system is a two stage problem; EEG recordings serve as input to a feature extraction phase, and then the features extracted are fed into a classifier. The features extraction methods exploited by researchers include; time-based features such as amplitude, duration, and sharpness [5]. Also frequency-based features such as fast Fourier transform [6], power spectral density [7]. The EEG signal being a non-stationary signal can be described by time-frequency features

Medical background

The role of EEG in the seizure detection process is very important compared to other approaches such as Magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) in terms of safety and cost. For an epilepsy diagnosed patient, the EEG recordings can belong to one of four categories; Pre-ictal refers to the state immediately before the actual seizure, though it has recently come to light that some of characteristics of this stage (such as visual auras) are actually the

Materials and methods

The proposed model includes a data acquisition phase followed by a pre-processing phase, a phase for segmenting each EEG channel into fixed length windows, a feature extraction phase where the Weighted Permutation Entropy (WPE) is estimated for each window, and finally a classification phase where EEG records are divided into one of two clinically significant EEG classes: (1) seizure and (2) no-seizure. The procedure of the proposed SVM classification for automated epileptic seizure detection

Model validation

Experiments have been carried out to validate the efficiency of the proposed seizure detection system and also its capability of detection and robustness to noise. Although the data set has five different classes of EEG segments (healthy with eyes open, healthy with eyes closed, interictal state obtained from epileptic zone, interictal state obtained from hippocampus zone of brain, and ictal state). The aim of this study is to detect segments with epileptic seizure activity so the multi-class

Classification of raw EEG signals

Fig. 7(a) shows the corresponding estimated WPE values (τ = 1, m = 3) for the entire 23.6 s EEG segments buffered in non-overlapping technique depicted in Fig. 2. The figure shows that the estimated WPE values are the lowest for EEG seizure segments (set S) compared to all the other datasets. A single Weighted Permutation Entropy value is calculated per window. For the overlapping segmentation techniques, same patterns are obtained that can be observed in Fig. 7(b). Furthermore, Fig. 8 shows

Discussion

An automated seizure detection model is introduced based on Weighted Permutation Entropy (WPE) and Support Vector Machine classifier (SVM). The experiments results show that the WPE values decrease during seizures which confirms the findings of previous studies that indicate that the brain activity during ictal state has a repetitive regular sequence and thus less chaos which yields to lower entropy values.

Comparing all obtained results, the best discrimination is obtained for seizure activity

Conclusion and future work

The proposed automated seizure detection model suggested scheme better tracks changes in the EEG signal through estimating the WPE value for each segment. Results show that the proposed model performed better than some existing schemes in literature according to the classification problem. The WPE algorithm was applied to raw and decomposed EEG signals. Another scenario was applied to simulate the real-time monitoring process, where the recording of EEG is accompanied by background noise

Noha Seddik Tawfik is teaching assistant at the Arab Academy for Science, Technology, and Maritime Transport, where she also obtained her M.Sc. and B.Sc degrees. Her research interests include biomedical engineering, pattern recognition and machine learning.

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The intelligent recognition of electroencephalogram (EEG) signals is a valuable tool for epileptic seizure classification. Given that visual inspection of EEG signals is time-consuming, and that mutant signals dramatically increase the workload of neurologists, automatic epilepsy diagnosis systems are extremely helpful. However, the existing epilepsy diagnosis methods suffer from some shortcomings. For example, they tend to fall into local optima quickly because of their failure to fully consider the discriminative features of EEG signals. To tackle this problem, in this article, an enhanced automatic epilepsy diagnosis method is proposed using time-frequency analysis and improved Harris hawks optimization (IHHO) with a hierarchical mechanism. Specifically, the signal is decomposed into five rhythms using continuous wavelet transform, with the local and global features extracted using the local binary pattern and the gray level co-occurrence matrix. Discriminative features are then selected and further mapped to the final recognition results using both IHHO and the k-nearest neighbor classifier. To evaluate its performance, the proposed method was compared with a variety of classical meta-heuristic algorithms on 23 benchmark functions. Moreover, the proposed approach achieved more than 99.67% accuracy on the Bonn dataset and 99.06% accuracy on the CHB-MIT dataset, out-performing a multitude of state-of-the-art methods. Taken together, these results demonstrate the utility of our approach in the automatic diagnosis of epilepsy. Supportive datasets and source codes for this research are publicly available at https://github.com/sstudying/lzzhen, and latest updates for the HHO algorithm are provided at https://aliasgharheidari.com/HHO.html.
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Citation Excerpt :
Compared to other methods, the proposed method shows almost 3 % improvement in classification accuracy of classification problem C1, similarly 3.55 % in classification problem C2, and 2.1 % in C3. In recent works [53,54,63,64], authors have used DWT to decompose EEG signals. The major drawback of DWT is that it uses a pre-fixed function for analysing signals which makes it non-adaptive.
Electroencephalogram (EEG) signals are non-linear and non-stationary in nature. The phase-space representation (PSR) method is useful for analysing the non-linear characteristics of EEG signals. Hence non-linear features based on a phase-space representation of EEG signals are effective in epileptic-seizure classification. In the past, various machine learning methods are used for classifying seizure EEG signals. However, they might fail to classify accurately due to complex data, so with effective non-linear features, ensemble learning classifiers can be investigated to improve the accuracy of an automated epileptic-seizure detection system. In this paper, the EEG signals are first decomposed in sub-bands using empirical wavelet transform (EWT) based on the Fourier Bessel series expansion (FBSE) which is termed as FBSE-EWT. Then, these sub-bands are reconstructed into three-dimensional (3D) PSR. Next, entropy-based features like line-length (LL), log-energy-entropy (LEEnt), and norm-entropy (NEnt) are computed from Euclidean distances of 3D PSR of sub-band signals. The extracted features are ranked based on p-values obtained from the Kruskal-Wallis statistical test for reducing the feature space. Experiments have been conducted using obtained ranked features on different ensemble learning classifiers, and the five best-performing classifiers are reported here, which are random forest (RF), extra tree (ET), extreme gradient boosting tree (xgBT), bagged-SVM (B-SVM), and bagged-k-nearest neighbours (B-k-NN), for classifying epileptic-seizure EEG signals. The performance of the proposed framework is evaluated using a publically accessible Bonn university EEG database for classifying epileptic-seizure EEG signals on well-known classification problems such as C₁ (seizure, normal), C₂, and C₃ (seizure, normal, and seizure-free). This dataset consists recording of 100 single-EEG signals from five seizure, seizure-free, and normal (healthy) subjects each. Model is trained and tested using 10-fold cross-validation to stave off from overfitting. The performance is also compared with other state-of-art methods. Obtained results confirm the superior performance of the proposed framework by achieving maximum classification accuracy of 100 %, 98.3 %, 97.8 % with ET, B-SVM, and ET classifiers from each studied classification problem, respectively. Hence, the proposed framework can assist medical professionals in analysing epileptic-seizure EEG signals more accurately.

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Sherin M. Youssef is professor, head of computer Engineering Department. She obtained her Ph.D., M.Phil. from University of Nottingham, UK (2004) in the field of intelligent systems. She did M.Sc. from University of Alexandria (1996) in machine learning & pattern analysis. Her research interest lies in the field of signal processing, video/image processing, image coding, intelligent systems, signal compression, biomedical engineering, biometrics, QoS intelligent networks, machine learning, genetic algorithms, neuro-based systems, swarm intelligence, multimedia systems.

Mohamed Kholief is an associate professor. He is the vice dean for academic and student affairs, College of Computing and Information Technology, at the Arab Academy for Science, Technology, and Maritime Transport in Alexandria, Egypt. He obtained his Ph.D. in computer science from Old Dominion University (Norfolk, Virginia) in 2003, and his M.Sc. and B.Sc. in computer science from Alexandria University (Egypt) in 1997 and 1991, respectively. His research interests include digital libraries, semantic web, e-learning and M-learning, software engineering, and knowledge Management.

^☆: Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. M. R. Daliri.

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A hybrid automated detection of epileptic seizures in EEG records☆

Highlights

Abstract

Introduction

Section snippets

Literature survey

Medical background

Materials and methods

Model validation

Classification of raw EEG signals

Discussion

Conclusion and future work

Expert Syst Appl

Comput Methods Programs Biomed

Expert Syst Appl

Biomed Signal Process Control

Biomed Signal Process Control

J Neurosci Methods

Expert Syst Appl

Comput Methods Programs Biomed

Biomed Signal Process Control

Int J Comput Electr Eng

Neuroimage

Pharmacoresistant epilepsy: from pathogenesis to current and emerging therapies

Clevel Clin J Med

Seizure detection: correlation of human experts

Clin Neurophysiol

Permutation entropy: a natural complexity measure for time series

Phys Rev Lett

Detection of interictal spikes and artifactual data through orthogonal transformations

Clin Neurophysiol

Classification of epilepti-form EEG using a hybrid system based on decision tree classifier and fast fourier transform

Appl Math Comput

Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: a comparative study